Findability Sciences predictive maintenance solution combines data from various sensors to identify/exhibits recognizable patterns or signatures and can spot trends resulting in potential fault detection and enable preventive maintenance actions. The self-learning algorithms can quickly classify such trends into appropriate categories to predict the potential failures and which components need immediate replacement or needs maintenance.
The key differentiation with our approach -
• Mixing of external data like weather & pollution
• The possibility of adding many other dependent variables based on use case from other data (e.g. ERP)
• Multi modeling technology provides more accuracy
• Different use-cases - At aircraft level, section level (e.g. an engine) or component level predictions.
• Through variable ranking used in models, know what is causing failures or need for maintenance.
• It’s a Self-learning, auto multi-modeling, fully automatic prediction – No Manual Intervention
11. Build future with AI
Vivek Vij
vivek@findabilitysciences.com
Direct: +1 781-353-2466
Findability Sciences 300 TravelCenters Drive STE 4690 Woburn MA 01801
Editor's Notes
Unprecedented amount of aircraft, airline-aviation operational data has opened up the potential for doing predictive maintenance – the capability to spot an emerging issue before it may impact schedule operations.
With progress in sensor technology and data processing techniques, structural health monitoring (SHM) can lead the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule
Unprecedented amount of aircraft, airline-aviation operational data has opened up the potential for doing predictive maintenance – the capability to spot an emerging issue before it may impact schedule operations.
With progress in sensor technology and data processing techniques, structural health monitoring (SHM) can lead the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule